COT-AD: Cotton Analysis Dataset
Akbar Ali, Mahek Vyas, Soumyaratna Debnath, Chanda Grover Kamra, Jaidev Sanjay Khalane, Reuben Shibu Devanesan, Indra Deep Mastan, Subramanian Sankaranarayanan, Pankaj Khanna, Shanmuganathan Raman
TL;DR
COT-AD is a large, annotated cotton crop dataset that supports various computer vision tasks to improve cotton disease detection, pest analysis, and crop management through diverse imaging modalities.
Contribution
The paper introduces COT-AD, a comprehensive cotton dataset with diverse annotations and images, filling a critical gap for data-driven cotton crop analysis.
Findings
Over 25,000 images collected across the cotton growth cycle
Includes aerial and high-resolution DSLR images with detailed annotations
Supports multiple computer vision tasks for cotton crop analysis
Abstract
This paper presents COT-AD, a comprehensive Dataset designed to enhance cotton crop analysis through computer vision. Comprising over 25,000 images captured throughout the cotton growth cycle, with 5,000 annotated images, COT-AD includes aerial imagery for field-scale detection and segmentation and high-resolution DSLR images documenting key diseases. The annotations cover pest and disease recognition, vegetation, and weed analysis, addressing a critical gap in cotton-specific agricultural datasets. COT-AD supports tasks such as classification, segmentation, image restoration, enhancement, deep generative model-based cotton crop synthesis, and early disease management, advancing data-driven crop management
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